One of the key challenges in the development of model transformations is the resolution of recurring semantic and syntactic heterogeneities. Thus, we provide a systematic classification of heterogeneities building upon a feature model that makes the interconnections between them explicit. On the basis of this classification, a set of benchmark examples was derived and used to evaluate current a...

Symbolic data structures such as Decision Diagrams have proved successful for model-checking. For high-level specifications such as those used in programming languages, especially when manipulating pointers or arrays, building and evaluating the transition is a challenging problem that limits wider applicability of symbolic methods. We propose a new symbolic algorithm, EquivSplit, allowing an e...

The growing dependence of modern society on telecommunication and information networks has become inevitable. Therefore, the security aspects of such networks play a strategic role in ensuring protection of data against misuse. Intrusion Detection systems (IDS) are meant to detect intruders who elude the “first line” protection. Data mining techniques are being used for building effective IDS. ...

Graph mining approaches are extremely popular and effective in molecular databases. The vast majority of these approaches first derive interesting, i.e. frequent, patterns and then use these as features to build predictive models. Rather than building these models in a two step indirect way, the SMIREP system introduced in this paper, derives predictive rule models from molecular data directly....

Building a classification system using datamining techniques has shown lower error rates than traditional algorithms such as decision trees. However, because the number of possible association rules in general is very large, algorithms are usually complicated and prone to overfitting. In this paper, we present a new algorithm that generates and uses a minimum set of association rules to form th...

Many problems in industry are a form of open-shop scheduling problem (OSSP). We describe a hybrid approach to this problem which combines a Genetic Algorithm (GA) with simple heuristic schedule building rules. Excellent performance is found on some benchmark OSS problems, including improvements on previous best-known results. We describe how our approach can be simply amended to deal with the m...

It is known that the performance of a genetic algorithm depends on the survival environment and the reproducibility of building blocks. In this paper, we propose a new encoding/crossover scheme that uses genic distance which explicitly defines the distance between each pair of genes in the chromosome. It pursues both relatively high survival probabilities of more epistatic gene groups and diver...

This paper completes a recent benchmark study in active structural control of a civil engineering structure. The previous work gave an analytical definition of a structure combined with actuators and sensors, along with 15 papers applying various control strategies to this common problem using the same evaluation and performance criteria. As actual implementation is the ultimate test of control...

We report the design and implementation of a callgraph profiler for GNU Octave, a numerical computing platform. GNU Octave simplifies matrix computation for use in modeling or simulation. Our work provides a callgraph profiler, which is an improvement on the flat profiler. We elaborate design constraints of building a profiler for numerical computation, and benchmark the profiler by comparing i...

We propose a novel supervised learning rule allowing the training of a precise input-output behavior to a spiking neuron. A single neuron can be trained to associate (map) different output spike trains to different multiple input spike trains. Spike trains are transformed into continuous functions through appropriate kernels and then Delta rule is applied. The main advantage of the method is it...